Effort rights‐based management
Bibliographic record
Abstract
Abstract Effort rights‐based fisheries management (RBM) is less widely used than catch rights, whether for groups or individuals. Because RBM on catch or effort necessarily requires a total allowable catch (TAC) or total allowable effort (TAE), RBM is discussed in conjunction with issues in assessing fish populations and providing TACs or TAEs. Both approaches have advantages and disadvantages, and there are trade‐offs between the two approaches. In a narrow economic sense, catch rights are superior because of the type of incentives created, but once the costs of research to improve stock assessments and the associated risks of determining the TAC and costs of monitoring, control, surveillance and enforcement are taken into consideration, the choice between catch or effort RBM becomes more complex and less clear. The results will be case specific. Hybrid systems based on both catch and effort are increasingly employed to manage marine fisheries to capture the advantages of both approaches. In hybrid systems, catch or effort RBM dominates and controls on the other supplements. RBM using either catch or effort by itself addresses only the target species stock externality and not the remaining externalities associated with by‐catch and the ecosystem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".